IIASA's Land use modelling tools & Applications in Brazil

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1 IIASA's Land use modelling tools & Applications in Brazil Michael Obersteiner, Program Director Ecosystems Services and Management IIASA CAPES/IIASA Workshop 25 th April, 2017 Brasilia, DF

2 Challenge: P:N ratio Growth rate

3 Land cover Land use Production Markets Demand Rain, Snow, Chemicals Below Root Zone EPIC Surface Flow 18 crops (FAO + SPAM) Wheat, Rice, Maize, Soybean, Barley, Sorghum, Millet, Cotton, Dry beans, Rapeseed, Groundnut, Sugarcane, Potatoes, Cassava, Sunflower, Chickpeas, Palm Fruit, Sweet potatoes 3 different systems Cropland Evaporation and Transpiration Subsurface Flow Population, GDP, preferences Food Fibers Energy ECONOMIC MARKET + Spatial equilibrium trade PRICES RUMINANT Digestibility model Feed intake Animal production GHG emissions 7 animals (FAO + Gridded livestock) Cattle & Buffalo Sheep & Goat Pig Poultry 8 different systems Grassland BIOENERGY Processing MJ biofuel MJ bioelectric Coproducts Land suitable for Poplar Pillow Eucalyptus Productivity from literature Short rotation plantations Global Land Cover 2000 Industry G4M Global Forest model Harvestable wood Harvesting costs Downscaled FAO FRA at grid level Area Carbon stock Age Tree size Species Rotation time Thinning Managed forest Natural forest Other natural land 3

4 DATA ISSUES

5 Cropland Disagreement

6 Cropland Capture Game Classified 2.5 million pixels in 7 weeks

7 Probably the most accurate cropland map See et al., PLOS 2014

8 Agricultural Management Practices 8

9 Crowdsourcing farming system info

10 towards a pest and disease EWS

11 Agri-Support Smartphone Application Startup screen and selection of crops in the application. The application allows users to take pictures which are enhanced with sensor information such as geo-location, compass direction and tilt of the phone while the picture was taken. The user can enter further information such as the crop type.

12 Agri-Support Smartphone Application Input fields for detailed information. Users can enter detailed information on the cultivation and harvest dates, field size, fertilizer and soil preparation, plant and seed information as well as information on pests and diseases.

13 BIOPHYSICAL MODELLING

14 Cropland The Biophysical Agriculture Model EPIC Processes Weather Hydrology Erosion Carbon sequestration Crop growth Crop rotations Fertilization (NPK) Tillage Irrigation Drainage Pesticide Grazing Manure Rain, Snow, Chemicals Below Root Zone EPIC Surface Flow Evaporation and Transpiration Subsurface Flow Major outputs: Crop yields, Environmental effects (e.g. soil carbon, P&N flows, )

15 EPIC HyperCube Production possibility sets irrigation intensity N fertilization

16 ACTIVITY COSTING

17 Cost Function Variables Definition 2/3 Variable Definition description unit Cost Variables 1 SDC costs of seeds EUR/ha 2 FRNC fertilizer nitrogen costs EUR/ha 3 FRPC fertilizer phosphorus costs EUR/ha 4 FRCO other costs of fertilization EUR/ha 5 TFRC =sum(2:4) total fertilizer costs EUR/ha 6 PPC cost of plant protection EUR/ha 7 IRGC irrigation costs EUR / ha 8 TFLC total fuel costs EUR/ha 9 TLAC total labor costs EUR / ha 10 OFAC other field activity costs EUR / ha 11 TFIN total cost of financing EUR / ha 12 TRC total transport costs EUR / ha 13 FARC costs for processes on farm EUR / ha 14 INFC infrastructure costs (cost of farm buildings) EUR / ha 15TPC =sum(1,5:14) total production costs EUR / ha 16TPCt =15/YLDG total production costs per ton EUR / t 17 LC land costs EUR / ha 18TC =TPC+LC total costs EUR / ha 19TCt =TC/YLDG total costs per ton EUR / t

18 Validating by typical farm survey in MT SOR QUE CNP PDL Km 18

19 Bottom-up farm level landscape scenarios for Mato Grosso, Alvaro Iribarrem, Aline Mosnier, Johannes Pirker, Bernardo Strassburg.

20 BAU downscaled

21 Scenario of intensification + improved timber production + FC compliance.

22 International Institute for Applied Systems Analysis December 15, 2016 Brazil s INDC Land-use change and forest sector Alexandre Ywata (IPEA) Aline Mosnier (IIASA) Aline Soterroni (INPE/IIASA) Fernando Ramos (INPE) Florian Kraxner (IIASA) Gilberto Camara (INPE) Johannes Pirker (IIASA) Michael Obersteiner (IIASA) Pedro Andrade (INPE) Petr Havlik (IIASA) Ricardo Souza (INPE) Rebecca Mant (UNEP-WCMC) Valerie Kapos (UNEP-WCMC)

23 Paris Agreement Paris climate agreement aims at holding global warming to well below 2 degrees Celsius and to pursue efforts to limit it to 1.5 degrees Celsius 190 countries submitted Intended Nationally Determined Contributions (INDCs) outlining their post-2020 climate action 28/04/

24 Brazilian Context Numbers Protected Areas per biome Amazon Caatinga Pantanal Cerrado Territory: 852 Mha Protected areas: 243 Mha (23%) Private properties: 572 Mha (67%) ~53% of Brazil s native forests are inside private properties Atlantic Forest Pampa 28/04/

25 Brazilian Context Global major producer 28/04/

26 Brazilian Context 1/3 of world s rainforests Antonio Scorza/Agence France-Presse Getty Images 28/04/

27 Brazilian Context Rich in Biodiversity Antonio Scorza/Agence France-Presse Getty Images 28/04/

28 Brazilian Context Emissions by Sector Source: SEEG Until middle of 2000s, the majority of Brazil s emissions was due to deforestation 28/04/

29 Brazilian Context Amazon Deforestation Deforestation (km 2 /year) Source: MMA 28/04/

30 Brazilian Context New Forest Code 28/04/

31 Brazil s INDC Contribution: Brazil intends to commit to reduce greenhouse gas emissions by 37% below 2005 levels in 2025 Subsequent indicative contribution: reduce greenhouse gas emissions by 43% below 2005 levels in 2030 Type: absolute target in relation to base year Coverage: 100% of the territory, economy- 28/04/

32 Brazil s INDC Further measures Biofuels : 18% in the energy mix by 2030 Energy sector: 45% of renewables in the energy mix by 2030 Agriculture sector: 15 Mha of degraded pasturelands by Mha of integrated cropland-livestock-forestry systems (ICLFS) by 2030 Industry sector: promote clean technology Transport sector: improve infrastructure 28/04/

33 Brazil s INDC Further measures Land Use Change and Forests Strengthening and enforcing the implementation of the Forest Code Zero illegal deforestation in the Amazon biome by 2030 Restoring and reforesting 12 million hectares (Mha) of forests by 2030 Enhancing sustainable native forest management system 28/04/

34 Brazil s Forest Code Legal Reserve requirement was established in 20% Legal Reserve requirement was DECREASED in Amazon region 80% 50% 35% 20% Amnesty of small farms (SFA) Quotas system (CRA) Environmental registry (CAR) Creation of Brazilian Forest Code Legal Reserve requirement was increased in Amazon region Yasuyoshi Chiba/AFP/Getty Images 28/04/

35 Brazil s Forest Code Creation of Brazilian Forest Code Legal Reserve requirement was established in 20% Legal Reserve requirement was increased in Amazon region Legal Reserve requirement was DECREASED in Amazon region Is the 2012 Forest Code enough to reconcile protection and production in Brazil? 28/04/ % 50% 35% 20% Amnesty of small farms (SFA) Quotas system (CRA) Environmental registry (CAR) Can Brazil achieve its INDC commitments by enforcing this environmental law? Yasuyoshi Chiba/AFP/Getty Images

36 In a nutshell our work on the Forest Code Why GLOBIOM? No ad-hoc scenarios but LUC as results of global demand & supply, within policy framework Able to capture indirect effects such as leakage Assumption: Full enforcement of FC measures! INDC: GoB took our results to Paris 28/04/

37 GLOBIOM: Global Biosphere Management Model Partial equilibrium model: Agriculture, Forestry and Bioenergy Population & Economic Growth & Exogenous Demand DEMAND SUPPLY SPATIAL RESOLUTION REGION MARKETS Wood Crops Livestock Forest Cropland Pasture Other LAND Commodity Prices and Quantities Land Use

38 GLOBIOM-Brazil: regional version of GLOBIOM 30 Regional zooming allows detailed spatial representation of land (50x50km) and introduction of regional policies

39 GLOBIOM-Brazil: regional version of GLOBIOM 30 Regional zooming allows detailed spatial representation of land (50x50km) and introduction of regional policies

40 Validation (Cumulated Deforestation ) PRODES* GLOBIOM-Brazil Mha Mha *PRODES: Amazon Deforestation Monitoring Project 28/04/

41 Forest Code Scenario 80% 50% 35% 20%» Legal Reserves (LR): defines the minimum percentage of forest to be preserved or restored on private properties at the landowner s expenses» Small Farm Amnesty (SFA): exempts the landowner from the need for restoration of LR areas illegally deforested before 2008 in small properties» The environment debt offset mechanism based on a tradable legal title of native vegetation surpluses mechanism (called CRA). Forest surplus on one property may be used to offset a legal reserve debt on another property within the same biome Source: IPAM 28/04/

42 Forest Code Scenario Measures of the Forest Code No enforcement of the Forest Code NoFC Full enforcement of the Forest Code FC Illegal deforestation control no yes Small farms amnesty (SFA) no yes Environmental reserve quota (CRA) no yes Forest regrowth no yes 28/04/

43 Forest loss (red) or gain (green) NoFC FC 28/04/

44 Production x Protection Stabilization of forest stocks as early as 2030 without much impact on the expansion of its agricultural production due to cattle intensification and conversion of other non-forest land. Cropland expansion is mainly driven by sugarcane, soybean and corn.

45 Forest Regrowth 28/04/2017

46 Emissions from land-use change and forestry 28/04/

47 Fragile Brazil s INDC Commitments? Deforestation in km 2 per year km 2 29% increase compared to % increase compared to 2012 Source: PRODES 28/04/

48 Thank you! 28/04/